• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.7
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• pp.655-663
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• 2017

With the development of ICT technology, the indoor utilization of robots is increasing. Research on transportation, cleaning, guidance robots, etc., that can be used now or increase the scope of future use will be advanced. To facilitate the use of mobile robots in indoor spaces, the problem of self-location recognition is an important research area to be addressed. If an unexpected collision occurs during the motion of a mobile robot, the position of the mobile robot deviates from the initially planned navigation path. In this case, the mobile robot needs a robust controller that enables the mobile robot to accurately navigate toward the goal. This research tries to address the issues related to self-location of the mobile robot. A robust position recognition system was implemented; the system estimates the position of the mobile robot using a combination of encoder information of the mobile robot and the absolute space coordinate transformation information obtained from external video sources such as a large number of CCTVs installed in the room. Furthermore, vector field histogram method of the pass traveling algorithm of the mobile robot system was applied, and the results of the research were confirmed after conducting experiments.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1328-1331
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• 2013

본 논문에서는 스마트폰 내장 가속도 센서를 이용한 2단계 행위 인식 시스템을 제안한다. 제안하는 행위 인식 시스템에서는 행위 별 시간에 따른 가속도 센서 데이터의 변화 패턴을 충분히 반영하기 위해, 1단계 분류에서는 결정트리 모델 학습과 분류를 수행하고, 2단계 분류에서는 1단계 분류 결과들의 시퀀스를 이용하여 HMM모델 학습과 분류를 수행하였다. 또한, 본 논문에서는 특정 사용자나 스마트폰의 특정 위치, 방향 변화에도 견고한 행위 인식을 위하여, 동일한 행위에 대해 사용자와 스마트폰의 위치, 방향을 변경하면서 다양한 훈련 데이터를 수집하였다. 6720개의 가속도 센서 데이터를 이용하여 총 6가지 실내 행위들을 인식하기 위한 실험들을 수행하였고, 그 결과 높은 인식 성능을 확인 할 수 있었다.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.57-65
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• 2018

Conventional RF signal-based indoor localization techniques such as BLE or Wi-Fi based fingerprinting method show considerable localization errors even in small-scale indoor environments due to unstable received signal strength(RSS) of RF signals. Therefore, it is difficult to apply the existing RF-based fingerprinting techniques to large-scale indoor environments such as airports and department stores. In this paper, instead of RF signal we use the geomagnetic sensor signal for indoor localization, whose signal strength is more stable than RF RSS. Although similar geomagnetic field values exist in indoor space, an object movement would experience a unique sequence of the geomagnetic field signals as the movement continues. We use a deep neural network model called the recurrent neural network (RNN), which is effective in recognizing time-varying sequences of sensor data, to track the user's location and movement path. To evaluate the performance of the proposed geomagnetic field based indoor positioning system (IPS), we constructed a magnetic field map for a campus testbed of about $94m{\times}26$ dimension and trained RNN using various potential movement paths and their location data extracted from the magnetic field map. By adjusting various hyperparameters, we could achieve an average localization error of 1.20 meters in the testbed.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.12
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• pp.1833-1840
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• 2013

A golf ball collecting robot in a golf ball driving range has been recently required because it is safer and more economic than a human being. In this paper, the golf ball recognition and distance estimation system based on a neural network and OpenCV is developed for the robot. The simulation results show that the recognition ratio is over 87% for the distance of less than 120cm and accurate rate for distance estimation is over 85% for golf balls in 30-180cm from a webcam.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.8C
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• pp.814-821
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• 2009

In an ultrawide band (UWB) indoor wireless localization, time of arrival (TOA) parameter estimation techniques have some difficulties in acquiring a reasonable TOA estimate because of the clustered multipath components overlapping or random time intervals mainly due to non line-of-sight (NLOS) environment. In order to solve that problem and achieve an excellent UWB indoor wireless localization, we propose a UWB signal model and a robust TOA parameter estimation technique that has little effect on the clustered problems unlike the conventional technique. Through simulation studies, the validity of the proposed model and the TOA estimation technique are examined. The performance of estimation error is also analyzed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1295-1302
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• 2010

This paper proposed indoor location recognition method based on RSSI(received signal strength indication) using the LVQ network. In order to verify the effectiveness of the proposed method, we performed experiments, and then compared to the conventional triangularity measurement method. In the experiments, we set up the system to the laboratory, divided the 40 section, and installed 6 nodes as a reference node. We obtained the log-normal path loss model of wireless channels, RSSI converted into the distance. The distance values used as the input of LVQ. To learn the LVQ network, we set the target values as section indices. In the experiments, we determined the optimal number of subclass, and confirmed that the success rate of training phase was 96%, test phase was 91%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.4
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• pp.991-998
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• 2010

In realization of wireless home network system using speech recognition in indoor voice recognition environment, background noise and reverberation are two main causes of digression in voice recognition system. In this study, the home network system resistant to reverberation and background noise using voice section detection method based on spectral entropy in indoor recognition environment is to be realized. Spectral subtraction can reduce the effect of reverberation and remove noise independent from voice signal by eliminating signal distorted by reverberation in spectrum. For effective spectral subtraction, the correct separation of voice section and silent section should be accompanied and for this, improvement of performance needs to be done, applying to voice section detection method based on entropy. In this study, experimental and indoor environment testing is carried out to figure out command recognition rate in indoor recognition environment. The test result shows that command recognition rate improved in static environment and reverberant room condition, using voice section detection method based on spectral entropy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.11
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• pp.2073-2078
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• 2016

Recently, there have been many research that recognize the situation using sensors. However, sensor data collection and analysis are still lacking in integration. This is because the data generated by the sensor is difficult to match in terms of metadata and units. Therefore, a methodology for efficiently using data generated from various sensors is needed. In this paper, we propose a system that recognizes the location through information generated from a moving iBeacon. This system constructs the ontology with the data that can recognize the exact position when the patient wearing iBeacon moves in the room. This maps standard items and sensor items, and stores the results of filtering the detected values as knowledge. the system can extract efficient location information by recognizing the value generated by moving the patient carrying iBeacon through the ontology. This can be applied not only to beacons but also to other sensors, and it can be applied variously according to the ontology configuration.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.129-130
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• 2016

실내 위치인식 기술인 핑거 프린트 기법을 이용해 출결을 확인함으로써 짧은 수업시간에 신속하고 정확하게 출석을 확인하는 것이 본 연구의 목적이다. 부정 출석을 막기 위해 핑거프린트를 이용한 실내 위치인식으로 학생들이 수업장소에 와있는지를 확인하고, 대리 출석을 막기 위해 출석확인을 하는 모바일기기의 고유 정보를 가져와 하나의 모바일 기기에서 한 번의 출석확인만 가능하도록 하였다. 또한 출석체크만 하고 수업을 빠져나가는 것을 방지하기 위해 수업 장소에 머무른 시간 또한 체크를 하였다. 향후 출석 시스템만이 아니라 출석 데이터로 출석이 성적에 미치는 영향 등 다양한 방면으로 활용될 수 있을 것이라 생각한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.1
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• pp.226-232
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• 2015

As the age of smart device has come, recently many application services related to smart phone are developing. The LBS(Location Based Service) technique is considered as one of the most important techniques to support location based application services. Usually the smart phone acquires the information of position by using the position recognition systems and sensors such as GPS(Global Positioning System) and G-Sensor. However, since the GPS signal from the satellite can hardly be received in the indoor environments, new LBS techniques for the indoor environment are required. In this paper, to solve the problem a position information transceiver using LED lights and smart phone camera sensor is proposed. We proved the possibility of the proposed positioning system through the experiments in the laboratory for the practical verification.